Information Retrieval Using Structured Resources for Paraphrase Resolution

ABSTRACT

An approach is provided in which a knowledge manager creates a pattern set based on training data that includes paraphrases and a set of first syntactic patterns. The knowledge manager receives a user question and matches one of the first syntactic patterns to a second syntactic pattern generated from the user question. Based on the matching, the knowledge manager generates new questions using the paraphrases in the pattern set and utilizes the new questions to query a second set of data and generate candidate answers that correspond to the user question.

BACKGROUND

The present disclosure relates to creating sets of paraphrases based ona structured resource and using the sets of paraphrases to generatesubsequent questions corresponding to a user question.

A traditional search engine produces the most accurate answers whenwords in a question match a passage in a corpus of documents in the sameorder. For example, if the traditional search engine receives a questionof “Who is the president of Company ABC?”, the traditional search engineproduces accurate results if the corpus includes a document with thepassage “Bill Smith is the president of Company ABC.” If a passage inthe corpus of documents does not closely match the words of the questionin order, the traditional search engine is less likely to produce anaccurate answer.

In reality, a corpus of documents may not have matching paraphrases, butrather have passages that include answers to a question such as “BillSmith leads Company ABC”, “The CEO of Company ABC is Bill Smith”, or“Company ABC's Chairperson is Bill Smith”. Unfortunately, thetraditional search engine may not detect information in these passagesto determine candidate answers.

BRIEF SUMMARY

According to one embodiment of the present disclosure, an approach isprovided in which a knowledge manager creates a pattern set based ontraining data that includes paraphrases and a set of first syntacticpatterns. The knowledge manager receives a user question and matches oneof the first syntactic patterns to a second syntactic pattern generatedfrom the user question. Based on the matching, the knowledge managergenerates new questions using the paraphrases in the pattern set andutilizes the new questions to query a second set of data and generatecandidate answers for the user question.

The foregoing is a summary and thus contains, by necessity,simplifications, generalizations, and omissions of detail; consequently,those skilled in the art will appreciate that the summary isillustrative only and is not intended to be in any way limiting. Otheraspects, inventive features, and advantages of the present disclosure,as defined solely by the claims, will become apparent in thenon-limiting detailed description set forth below.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The present disclosure may be better understood, and its numerousobjects, features, and advantages made apparent to those skilled in theart by referencing the accompanying drawings, wherein:

FIG. 1 is a block diagram of a data processing system in which themethods described herein can be implemented; and

FIG. 2 provides an extension of the information handling systemenvironment shown in FIG. 1 to illustrate that the methods describedherein can be performed on a wide variety of information handlingsystems which operate in a networked environment;

FIG. 3 is an exemplary diagram depicting a knowledge manager that groupssemantically similar paraphrases into pattern sets and utilizes thesemantically similar paraphrases to generate new questions for use in atraditional search engine;

FIG. 4 is a diagram depicting a pattern set generator groupingsemantically similar paraphrases and corresponding syntactic patternsinto pattern sets;

FIG. 5 is an exemplary diagram depicting a training pattern generatorcreating training syntactic patterns and paraphrases based upon trainingdata;

FIG. 6 is an exemplary diagram depicting a semantically similar questiongenerator matching a user question's syntactic pattern to a syntacticpattern in a pattern set and generating new questions based uponparaphrases included in the pattern set;

FIG. 7 is an exemplary flowchart showing steps taken by a knowledgemanager to generate pattern sets from training data;

FIG. 8 is an exemplary flowchart showing steps taken by a knowledgemanager to match a question's syntactic pattern to semantic patterns ina pattern set and generate new questions based upon paraphrases found ina pattern set corresponding to a matched syntactic pattern; and

FIG. 9 is an exemplary flowchart showing steps taken to generate newquestions from paraphrases and perform a query on a resource.

DETAILED DESCRIPTION

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the disclosure.As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present disclosure has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the disclosure in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure. Theembodiment was chosen and described in order to best explain theprinciples of the disclosure and the practical application, and toenable others of ordinary skill in the art to understand the disclosurefor various embodiments with various modifications as are suited to theparticular use contemplated.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions. The following detailed description willgenerally follow the summary of the disclosure, as set forth above,further explaining and expanding the definitions of the various aspectsand embodiments of the disclosure as necessary.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion/answer creation (QA) system 100 in a computer network 102.Knowledge manager 100 may include a computing device 104 (comprising oneor more processors and one or more memories, and potentially any othercomputing device elements generally known in the art including buses,storage devices, communication interfaces, and the like) connected tothe computer network 102. The network 102 may include multiple computingdevices 104 in communication with each other and with other devices orcomponents via one or more wired and/or wireless data communicationlinks, where each communication link may comprise one or more of wires,routers, switches, transmitters, receivers, or the like. Knowledgemanager 100 and network 102 may enable question/answer (QA) generationfunctionality for one or more content users. Other embodiments ofknowledge manager 100 may be used with components, systems, sub-systems,and/or devices other than those that are depicted herein.

Knowledge manager 100 may be configured to receive inputs from varioussources. For example, knowledge manager 100 may receive input from thenetwork 102, a corpus of electronic documents 106 or other data, acontent creator 108, content users, and other possible sources of input.In one embodiment, some or all of the inputs to knowledge manager 100may be routed through the network 102. The various computing devices 104on the network 102 may include access points for content creators andcontent users. Some of the computing devices 104 may include devices fora database storing the corpus of data. The network 102 may include localnetwork connections and remote connections in various embodiments, suchthat knowledge manager 100 may operate in environments of any size,including local and global, e.g., the Internet. Additionally, knowledgemanager 100 serves as a front-end system that can make available avariety of knowledge extracted from or represented in documents,network-accessible sources and/or structured resource sources. In thismanner, some processes populate the knowledge manager with the knowledgemanager also including input interfaces to receive knowledge requestsand respond accordingly.

In one embodiment, the content creator creates content in a document 106for use as part of a corpus of data with knowledge manager 100. Thedocument 106 may include any file, text, article, or source of data foruse in knowledge manager 100. Content users may access knowledge manager100 via a network connection or an Internet connection to the network102, and may input questions to knowledge manager 100 that may beanswered by the content in the corpus of data. As further describedbelow, when a process evaluates a given section of a document forsemantic content, the process can use a variety of conventions to queryit from the knowledge manager. One convention is to send a well-formedquestion. Semantic content is content based on the relation betweensignifiers, such as words, phrases, signs, and symbols, and what theystand for, their denotation, or connotation. In other words, semanticcontent is content that interprets an expression, such as by usingNatural Language (NL) Processing. In one embodiment, the process sendswell-formed questions (e.g., natural language questions, etc.) to theknowledge manager. Knowledge manager 100 may interpret the question andprovide a response to the content user containing one or more answers tothe question. In some embodiments, knowledge manager 100 may provide aresponse to users in a ranked list of answers.

In some illustrative embodiments, knowledge manager 100 may be the IBMWatson™ QA system available from International Business MachinesCorporation of Armonk, New York, which is augmented with the mechanismsof the illustrative embodiments described hereafter. The IBM Watson™knowledge manager system may receive an input question which it thenparses to extract the major features of the question, that in turn arethen used to formulate queries that are applied to the corpus of data.Based on the application of the queries to the corpus of data, a set ofhypotheses, or candidate answers to the input question, are generated bylooking across the corpus of data for portions of the corpus of datathat have some potential for containing a valuable response to the inputquestion.

The IBM Watson™ QA system then performs deep analysis on the language ofthe input question and the language used in each of the portions of thecorpus of data found during the application of the queries using avariety of reasoning algorithms. There may be hundreds or even thousandsof reasoning algorithms applied, each of which performs differentanalysis, e.g., comparisons, and generates a score. For example, somereasoning algorithms may look at the matching of terms and synonymswithin the language of the input question and the found portions of thecorpus of data. Other reasoning algorithms may look at temporal orspatial features in the language, while others may evaluate the sourceof the portion of the corpus of data and evaluate its veracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the IBM Watson™ QA system. Thestatistical model may then be used to summarize a level of confidencethat the IBM Watson™ QA system has regarding the evidence that thepotential response, i.e. candidate answer, is inferred by the question.This process may be repeated for each of the candidate answers until theIBM Watson™ QA system identifies candidate answers that surface as beingsignificantly stronger than others and thus, generates a final answer,or ranked set of answers, for the input question. More information aboutthe IBM Watson™ QA system may be obtained, for example, from the IBMCorporation website, IBM Redbooks, and the like. For example,information about the IBM Watson™ QA system can be found in Yuan et al.,“Watson and Healthcare,” IBM developerWorks, 2011 and “The Era ofCognitive Systems: An Inside Look at IBM Watson and How it Works” by RobHigh, IBM Redbooks, 2012.

Types of information handling systems that can utilize knowledge manager100 range from small handheld devices, such as handheld computer/mobiletelephone 110 to large mainframe systems, such as mainframe computer170. Examples of handheld computer 110 include personal digitalassistants (PDAs), personal entertainment devices, such as MP3 players,portable televisions, and compact disc players. Other examples ofinformation handling systems include pen, or tablet, computer 120,laptop, or notebook, computer 130, personal computer system 150, andserver 160. As shown, the various information handling systems can benetworked together using computer network 100. Types of computer network102 that can be used to interconnect the various information handlingsystems include Local Area Networks (LANs), Wireless Local Area Networks(WLANs), the Internet, the Public Switched Telephone Network (PSTN),other wireless networks, and any other network topology that can be usedto interconnect the information handling systems. Many of theinformation handling systems include nonvolatile data stores, such ashard drives and/or nonvolatile memory. Some of the information handlingsystems shown in FIG. 1 depicts separate nonvolatile data stores (server160 utilizes nonvolatile data store 165, and mainframe computer 170utilizes nonvolatile data store 175. The nonvolatile data store can be acomponent that is external to the various information handling systemsor can be internal to one of the information handling systems. Anillustrative example of an information handling system showing anexemplary processor and various components commonly accessed by theprocessor is shown in FIG. 2.

FIG. 2 illustrates information handling system 200, more particularly, aprocessor and common components, which is a simplified example of acomputer system capable of performing the computing operations describedherein. Information handling system 200 includes one or more processors210 coupled to processor interface bus 212. Processor interface bus 212connects processors 210 to Northbridge 215, which is also known as theMemory Controller Hub (MCH). Northbridge 215 connects to system memory220 and provides a means for processor(s) 210 to access the systemmemory. Graphics controller 225 also connects to Northbridge 215. In oneembodiment, PCI Express bus 218 connects Northbridge 215 to graphicscontroller 225. Graphics controller 225 connects to display device 230,such as a computer monitor.

Northbridge 215 and Southbridge 235 connect to each other using bus 219.In one embodiment, the bus is a Direct Media Interface (DMI) bus thattransfers data at high speeds in each direction between Northbridge 215and Southbridge 235. In another embodiment, a Peripheral ComponentInterconnect (PCI) bus connects the Northbridge and the Southbridge.Southbridge 235, also known as the I/O Controller Hub (ICH) is a chipthat generally implements capabilities that operate at slower speedsthan the capabilities provided by the Northbridge. Southbridge 235typically provides various busses used to connect various components.These busses include, for example, PCI and PCI Express busses, an ISAbus, a System Management Bus (SMBus or SMB), and/or a Low Pin Count(LPC) bus. The LPC bus often connects low-bandwidth devices, such asboot ROM 296 and “legacy” I/O devices (using a “super I/O” chip). The“legacy” I/O devices (298) can include, for example, serial and parallelports, keyboard, mouse, and/or a floppy disk controller. The LPC busalso connects Southbridge 235 to Trusted Platform Module (TPM) 295.Other components often included in Southbridge 235 include a DirectMemory Access (DMA) controller, a Programmable Interrupt Controller(PIC), and a storage device controller, which connects Southbridge 235to nonvolatile storage device 285, such as a hard disk drive, using bus284.

ExpressCard 255 is a slot that connects hot-pluggable devices to theinformation handling system. ExpressCard 255 supports both PCI Expressand USB connectivity as it connects to Southbridge 235 using both theUniversal Serial Bus (USB) the PCI Express bus. Southbridge 235 includesUSB Controller 240 that provides USB connectivity to devices thatconnect to the USB. These devices include webcam (camera) 250, infrared(IR) receiver 248, keyboard and trackpad 244, and Bluetooth device 246,which provides for wireless personal area networks (PANs). USBController 240 also provides USB connectivity to other miscellaneous USBconnected devices 242, such as a mouse, removable nonvolatile storagedevice 245, modems, network cards, ISDN connectors, fax, printers, USBhubs, and many other types of USB connected devices. While removablenonvolatile storage device 245 is shown as a USB-connected device,removable nonvolatile storage device 245 could be connected using adifferent interface, such as a Firewire interface, etcetera.

Wireless Local Area Network (LAN) device 275 connects to Southbridge 235via the PCI or PCI Express bus 272. LAN device 275 typically implementsone of the IEEE .802.11 standards of over-the-air modulation techniquesthat all use the same protocol to wireless communicate betweeninformation handling system 200 and another computer system or device.Optical storage device 290 connects to Southbridge 235 using Serial ATA(SATA) bus 288. Serial ATA adapters and devices communicate over ahigh-speed serial link. The Serial ATA bus also connects Southbridge 235to other forms of storage devices, such as hard disk drives. Audiocircuitry 260, such as a sound card, connects to Southbridge 235 via bus258. Audio circuitry 260 also provides functionality such as audioline-in and optical digital audio in port 262, optical digital outputand headphone jack 264, internal speakers 266, and internal microphone268. Ethernet controller 270 connects to Southbridge 235 using a bus,such as the PCI or PCI Express bus. Ethernet controller 270 connectsinformation handling system 200 to a computer network, such as a LocalArea Network (LAN), the Internet, and other public and private computernetworks.

While FIG. 2 shows one information handling system, an informationhandling system may take many forms, some of which are shown in FIG. 1.For example, an information handling system may take the form of adesktop, server, portable, laptop, notebook, or other form factorcomputer or data processing system. In addition, an information handlingsystem may take other form factors such as a personal digital assistant(PDA), a gaming device, ATM machine, a portable telephone device, acommunication device or other devices that include a processor andmemory.

FIGS. 3 through 9 depict an approach that can be executed on aninformation handling system. The information handling system analyzestraining data against a structured resource and creates pattern setsthat each include semantically similar paraphrases and correspondingsyntactic patterns. The information handling system also includesparaphrase scores in the pattern sets that are based on a set of firstcandidate answers obtained from querying the structured resource. Whenthe information handling system receives a user question, theinformation handling system generates a syntactic pattern of thequestion and compares the generated syntactic pattern to the syntacticpatterns in the pattern sets. When a match is found, the informationhandling system retrieves paraphrases from the pattern set containingthe matched syntactic pattern. The information handling system thengenerates new questions based on the retrieved paraphrases and utilizesthe new questions to create a second query that queries a second set ofdata, such as an unstructured resource, which identifies a set ofcandidate answers. In turn, the information handling system scores theset of second candidate answers based on the paraphrase scorescorresponding to the paraphrases utilized to identify the candidateanswers.

FIG. 3 is an exemplary diagram depicting a knowledge manager that groupssemantically similar paraphrases into pattern sets and utilizes thesemantically similar paraphrases to generate new questions for use in atraditional search engine.

Knowledge manager 100 receives training data 310 from subject matterexperts 300. In one embodiment, training data 310 includes trainingquestion/answer pairs. In another embodiment, training data 310 includestraining sentences or statements that encompass training questions andanswers. For each training pair, pattern set generator 320 generates atraining syntactic pattern, a training paraphrase, and a training query.In one embodiment, pattern set generator 320 uses knowledge base 106,which includes structured resource data, to generate the trainingqueries.

Pattern set generator 320 compares a newly generated training queryagainst previously generated training queries stored in pattern setstore to determine whether a match exists. If a match exists between anewly generated training query and a previously generated trainingquery, pattern set generator 320 stores, in pattern set store 330, acorresponding new training paraphrase and a new training syntacticpattern in a pattern set corresponding to the matched previouslygenerated training query. Once pattern set generator 320 finishesevaluating training data 310, pattern set store 330 stores pattern setsthat each include multiple syntactic patterns and correspondingsemantically similar paraphrases. For example, one of pattern set store330's pattern sets may include semantically similar paraphrases such as“Y is the CEO of X”, “Y leads X”, and “Y is the boss of X” (see FIG. 4and corresponding text for further details).

In one embodiment, pattern set generator 320 assigns paraphrase scoresto paraphrases, which indicate a relative accuracy of a correspondingtraining query to the training pair when the training query queriesknowledge base 106. For example, if a training question is “Whatcountries border the United States?” and the training query returned“Canada”, the corresponding paraphrase may be assigned a score of 50%.

A user question, such as question X 360, is provided through GUI 350.Question X 360 may not be related to knowledge base 106. Semanticallysimilar question generator 340 creates a syntactic pattern from questionX 360 and compares the syntactic pattern against syntactic patternsstored in pattern store 330. When semantically similar questiongenerator 340 finds a match in a pattern set, semantically similarquestion generator 340 retrieves paraphrases from the matching patternset and generates new questions 370.

In turn, semantically similar question generator 340 provides question X360 and new questions 370 to traditional search engine 380. Traditionalsearch engine 380 generates queries based on the received questions andqueries resource data in source data 390, which corresponds to questionX 360. For example, if question X 360 is “How fast does the Ford Mustanggo,” source data 390 may include unstructured data obtained fromautomotive articles. In turn, traditional search engine 380 providescandidate answers 395 resulting from the query to the user through GUI350. In one embodiment, the candidate answers 395 may be scored basedupon the paraphrase scores assigned to the paraphrases by pattern setgenerator 320 discussed above (see FIGS. 7 through 9 and correspondingtext for further details).

FIG. 4 is a diagram depicting a pattern set generator groupingsemantically similar paraphrases and corresponding syntactic patternsinto pattern sets. Pattern set generator 320 receives training data thatincludes training question 400 and training answer 410. Training patterngenerator 420, in one embodiment, uses training question 400 to generatea focus phrase. In this embodiment, training pattern generator 420 thenuses the focus phrase to generate training syntactic pattern 430 basedupon the syntactic relationships between training entities within thefocus phrase. Training pattern generator 420 also generates trainingparaphrase 440 based on training question 400.

Training query generator 450 identifies database paths in knowledge base106 (e.g., a structured resource) that the training question 400 totraining answer 410. For example, training query generator 450 finds adatabase path between the top speed of the F-14 Tomcat and Mach 2.3 inknowledge base 106. In turn, training query generator 450 generatestraining query 460.

In one embodiment, training query generator 450 queries knowledge base106 using training query 460 and compares the returned answers withtraining answer 410 to generate a training score. For example, if atraining question is “What countries boarder the United States,” thetraining answers include Canada and Mexico. In this example, if atraining query returned only Canada, the training score would not be ashigh as a training query that returned both Canada and Mexico. Trainingquery generator 450, in turn, produces training score 465, which areassigned to training paraphrase 440 by pattern mapper 470 (discussedbelow).

Pattern mapper 470 compares training query 460 with previously generatedtraining queries in in pattern set store 330 to identify matches orcorrelations. A match or correlation between training queries indicatessemantic similarities between paraphrases within the pattern set. Whenpattern mapper 470 detects a match or correlation, pattern mapper 470stores training syntactic pattern 430, training paraphrase 440, andtraining score 465 in the pattern set corresponding to the matchedpreviously generated training query.

However, when pattern mapper 470 does not detect a match or correlation,pattern mapper 470 creates a new pattern set and stores trainingsyntactic pattern 430, training paraphrase 440, training score 465, andtraining query 460 in the new pattern set (see FIG. 7 and correspondingtext for further details). FIG. 4 shows that pattern set store 330includes two pattern sets A and B, each of which includes threesyntactic patterns and corresponding paraphrases/scores.

FIG. 5 is an exemplary diagram depicting a training pattern generatorcreating training syntactic patterns and paraphrases based upon trainingdata.

Training data 330 includes three training pairs 500, 510, and 520. Fromtraining data 330, training pattern generator 420 generates three setsof training syntactic patterns and corresponding paraphrases, which areincluded in entries 530, 540, and 550. As such, when a user question isreceived whose syntactic pattern matches one of the syntactic patternsin entries 530, 540, or 550, semantically similar question generator 340generates new questions based upon the paraphrases in entries 530, 540,and 550. For example, if a user question is “How fast is the FordMustang?”, semantically similar question generator 340 replaces X with“The Ford Mustang” and generates new questions: “What is the top speedof the Ford Mustang?,” “The Ford mustang goes what?,” and “The Fordmustang can reach what?” (see FIG. 6 and corresponding text for furtherdetails).

FIG. 6 is an exemplary diagram depicting a semantically similar questiongenerator matching a user question's syntactic pattern to a syntacticpattern in a pattern set and generating new questions based uponparaphrases included in the pattern set.

Question pattern generator 600 receives question X 360 and generatessyntactic pattern X 605. Paraphrase mapper 610 compares pattern X 605against syntactic patterns in pattern set store 330. When paraphrasemapper 610 detects a match, such as pattern X=pattern A1 shown in FIG.6, paraphrase mapper 610 retrieves paraphrases from the matching patternset. As can be seen, paraphrase mapper 610 retrieves paraphrases A1, A2and A3 because pattern X=pattern A1 which is in pattern set A.

In turn, paraphrase mapper 610 generates new questions 370 fromparaphrases A1, A2 and A3 by replacing variables in the paraphrases withcorresponding nouns, verbs, etc. in question X 360. For example, ifquestion X is “How fast does the Ford Mustang go” and paraphrase A1 is“What is the X of Y,” paraphrase mapper generates the question of “Whatis the top speed of the Ford Mustang?”

Traditional search engine 380 generates queries 620 based on question X360 and new questions 370. In turn, traditional search engine 380 scoresthe candidate answers from the queries, such as based on paraphrasescores previously assigned to the paraphrases, and provides answers 395to a user through GUI 350.

FIG. 7 is an exemplary flowchart showing steps taken by a knowledgemanager to generate pattern sets from training data. FIG. 7 processingcommences at 700 whereupon, at step 710, the process selects a firsttraining pair, training sentence, or training statement. At step 720,the process generates a training syntactic pattern and a trainingparaphrase from a training question included in the training pair.

At step 730, the process generates a training query based upon thetraining pair when evaluated against knowledge base 106 as discussedpreviously. In one embodiment, the process assigns a paraphrase score tothe training paraphrase based upon comparing training answers receivedfrom the training query against training answers in the training pair.

At step 740, the process compares the generated training query againstpreviously generated training queries in pattern set store 330. Theprocess determines as to whether the generated training query matches(or correlates) to one of the previously generated training query(decision 750). If the generated training query does not match one ofthe previously generated training queries, then decision 750 branches tothe ‘no’ branch. At step 760, the process creates a new pattern set andadds the training syntactic pattern, paraphrase score, trainingparaphrase, and training query to the new pattern set.

On the other hand, if the generated training query matches one of thepreviously generated training queries, then decision 750 branches to the‘yes’ branch. At step 770, the process adds the training syntacticpattern, paraphrase score, and training paraphrase to the pattern setthat includes the matched training query.

The process determines as to whether to continue (decision 780). If theprocess should continue, then decision 780 branches to the ‘yes’ branchwhich loops back to select and process the next training pair. Thislooping continues until each training pair has been processed, at whichpoint decision 780 branches to the ‘no’ branch exiting the loop. FIG. 7processing thereafter ends at 795.

FIG. 8 is an exemplary flowchart showing steps taken by a knowledgemanager to match a question's syntactic pattern to semantic patterns ina pattern set and generate new questions based upon paraphrases found ina pattern set corresponding to a matched syntactic pattern.

FIG. 8 processing commences at 800 whereupon, at step 810, the processreceives a question and at step 820, generates a question semanticpattern from the received question. At step 825, the process comparesthe question semantic pattern against pattern set semantic patternsfound in pattern set store 330.

The process determines as to whether a match (or correlation) existsbetween the question semantic pattern and sematic patterns found inpattern store 330 (decision 830). If a match is not found, then decision830 branches to the ‘no’ branch and the process sends the question to atraditional search engine to process. FIG. 8 processing thereafter endsat 850.

On the other hand, if a match is found between the question semanticpattern and one of the sematic patterns stored in pattern set store 330,then decision 830 branches to the ‘yes’ branch. At step 860, the processretrieves paraphrases from the pattern set that includes the matchedsemantic pattern. At step 870, the process generates new questions basedon the retrieved paraphrases and the original question as discussedpreviously.

At step 875, the process provides new questions and the original userquestion to a traditional search engine to perform a query. Atpredefined process 880, the process generates new questions from theparaphrases and queries a resource on source data 390 using the newlygenerated questions. (see FIG. 8 and corresponding text for processingdetails). Source data 390 may include, for example, unstructured datathat corresponds to the original question. At step 890, the processprovides scored answers to a user and FIG. 8 processing thereafter endsat 895.

FIG. 9 is an exemplary flowchart showing steps taken to generate newquestions from paraphrases and perform a query on a resource. FIG. 9processing commences at 900 whereupon, at step 910, the processgenerates queries based upon the original user question and the newquestions generated as discussed earlier. At step 920, the processqueries source data 390 using the generated queries and identifiesdocument passages that are returned by the query.

At step 930, the process selects the first matching document passageand, at step 940, the process scores the matched document passage basedupon the matching paraphrase's paraphrase score, which was generated atstep 730 during the training stage.

The process determines as to whether there are more matching documentpassages to evaluate (decision 950). If there are more matching documentpassages to evaluate, then decision 950 branches to the ‘yes’ branchwhich loops back to select the next document passage. This loopingcontinues until there are no more document passages to evaluate, atwhich point decision 950 branches to the ‘no’ branch exiting the loop.This looping continues until there are no more document passages toevaluate, at which point decision 950 branches to the ‘no’ branchexiting the loop. FIG. 9 processing thereafter ends at 960.

While particular embodiments of the present disclosure have been shownand described, it will be obvious to those skilled in the art that,based upon the teachings herein, that changes and modifications may bemade without departing from this disclosure and its broader aspects.Therefore, the appended claims are to encompass within their scope allsuch changes and modifications as are within the true spirit and scopeof this disclosure. Furthermore, it is to be understood that thedisclosure is solely defined by the appended claims. It will beunderstood by those with skill in the art that if a specific number ofan introduced claim element is intended, such intent will be explicitlyrecited in the claim, and in the absence of such recitation no suchlimitation is present. For non-limiting example, as an aid tounderstanding, the following appended claims contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimelements. However, the use of such phrases should not be construed toimply that the introduction of a claim element by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim element to disclosures containing only one suchelement, even when the same claim includes the introductory phrases “oneor more” or “at least one” and indefinite articles such as “a” or “an”;the same holds true for the use in the claims of definite articles.

1. A method implemented by an information handling system that includesa memory and a processor, the method comprising: generating a patternset that comprises a plurality of paraphrases and a plurality of firstsyntactic patterns, wherein the pattern set corresponds to a first setof resource data; creating one or more new questions based on one ormore of the plurality of paraphrases in response to matching one of theplurality of first syntactic patterns to a second syntactic patterngenerated from a user question; utilizing the one or more new questionsto query a second set of resource data; and generating, based upon thequery, one or more candidate answers to the user question.
 2. The methodof claim 1 wherein creating the pattern set further comprises:retrieving a first set of training data, wherein the first set oftraining data is selected from the group consisting of a trainingquestion/answer pair and a training statement; generating a first querybased upon the first set of training data and the first set of resourcedata; creating a selected one of the plurality of first syntacticpatterns and a selected one of the plurality of paraphrases based uponthe first set of training data; and adding the first query, the selectedfirst syntactic pattern, and the selected paraphrase to the pattern set.3. The method of claim 2 further comprising: retrieving a second set oftraining data; creating a training syntactic pattern, a trainingparaphrase, and a training query based on the second set of trainingdata; comparing the training query to the first query; and in responseto determining that the training query correlates to the first query,adding the training syntactic pattern and the training paraphrase to thepattern set.
 4. The method of claim 3 further comprising: in response tothe training query not correlating to the first query, creating a newpattern set; and adding the training syntactic pattern, the trainingparaphrase, and the training query to the new pattern set.
 5. The methodof claim 2 further comprising: adding a paraphrase score to the selectedparaphrase based upon one or more training query results generated fromutilizing the first query to query the first set of resource data; andscoring at least one of the one or more candidate answers based upon theparaphrase score.
 6. The method of claim 1 wherein the first set ofresource data is structured data, and wherein the second set of resourcedata is unstructured data.
 7. The method of claim 1 wherein thegenerating of the one or more new questions further comprises: selectingone or more terms in the user question; and replacing one or morevariables in at least one of the plurality of paraphrases with theselected one or more terms.
 8. An information handling systemcomprising: one or more processors; a memory coupled to at least one ofthe processors; and a set of computer program instructions stored in thememory and executed by at least one of the processors in order toperform actions of: generating a pattern set that comprises a pluralityof paraphrases and a plurality of first syntactic patterns, wherein thepattern set corresponds to a first set of resource data; creating one ormore new questions based on one or more of the plurality of paraphrasesin response to matching one of the plurality of first syntactic patternsto a second syntactic pattern generated from a user question; utilizingthe one or more new questions to query a second set of resource data;and generating, based upon the query, one or more candidate answers tothe user question.
 9. The information handling system of claim 8 whereinat least one of the one or more processors perform additional actionscomprising: retrieving a first set of training data, wherein the firstset of training data is selected from the group consisting of a trainingquestion/answer pair and a training statement; generating a first querybased upon the first set of training data and the first set of resourcedata; creating a selected one of the plurality of first syntacticpatterns and a selected one of the plurality of paraphrases based uponthe first set of training data; and adding the first query, the selectedfirst syntactic pattern, and the selected paraphrase to the pattern set.10. The information handling system of claim 9 wherein at least one ofthe one or more processors perform additional actions comprising:retrieving a second set of training data; creating a training syntacticpattern, a training paraphrase, and a training query based on the secondset of training data; comparing the training query to the first query;and in response to determining that the training query correlates to thefirst query, adding the training syntactic pattern and the trainingparaphrase to the pattern set.
 11. The information handling system ofclaim 10 wherein at least one of the one or more processors performadditional actions comprising: in response to the training query notcorrelating to the first query, creating a new pattern set; and addingthe training syntactic pattern, the training paraphrase, and thetraining query to the new pattern set.
 12. The information handlingsystem of claim 9 wherein at least one of the one or more processorsperform additional actions comprising: adding a paraphrase score to theselected paraphrase based upon one or more training query resultsgenerated from utilizing the first query to query the first set ofresource data; and scoring at least one of the one or more candidateanswers based upon the paraphrase score.
 13. The information handlingsystem of claim 8 wherein the first set of resource data is structureddata, and wherein the second set of resource data is unstructured data.14. The information handling system of claim 8 wherein at least one ofthe one or more processors perform additional actions comprising:selecting one or more terms in the user question; and replacing one ormore variables in at least one of the plurality of paraphrases with theselected one or more terms.
 15. A computer program product stored in acomputer readable storage medium, comprising computer program code that,when executed by an information handling system, causes the informationhandling system to perform actions comprising: generating a pattern setthat comprises a plurality of paraphrases and a plurality of firstsyntactic patterns, wherein the pattern set corresponds to a first setof resource data; creating one or more new questions based on one ormore of the plurality of paraphrases in response to matching one of theplurality of first syntactic patterns to a second syntactic patterngenerated from a user question; utilizing the one or more new questionsto query a second set of resource data; and generating, based upon thequery, one or more candidate answers to the user question.
 16. Thecomputer program product of claim 15 wherein the information handlingsystem performs additional actions comprising: retrieving a first set oftraining data, wherein the first set of training data is selected fromthe group consisting of a training question/answer pair and a trainingstatement; generating a first query based upon the first set of trainingdata and the first set of resource data; creating a selected one of theplurality of first syntactic patterns and a selected one of theplurality of paraphrases based upon the first set of training data; andadding the first query, the selected first syntactic pattern, and theselected paraphrase to the pattern set.
 17. The computer program productof claim 16 wherein the information handling system performs additionalactions comprising: retrieving a second set of training data; creating atraining syntactic pattern, a training paraphrase, and a training querybased on the second set of training data; comparing the training queryto the first query; and in response to determining that the trainingquery correlates to the first query, adding the training syntacticpattern and the training paraphrase to the pattern set.
 18. The computerprogram product of claim 17 wherein the information handling systemperforms additional actions comprising: in response to the trainingquery not correlating to the first query, creating a new pattern set;and adding the training syntactic pattern, the training paraphrase, andthe training query to the new pattern set.
 19. The computer programproduct of claim 16 wherein the information handling system performsadditional actions comprising: adding a paraphrase score to the selectedparaphrase based upon one or more training query results generated fromutilizing the first query to query the first set of resource data; andscoring at least one of the one or more candidate answers based upon theparaphrase score.
 20. The computer program product of claim 15 whereinthe information handling system performs additional actions comprising:selecting one or more terms in the user question; and replacing one ormore variables in at least one of the plurality of paraphrases with theselected one or more terms.